Adaptive Genetic Algorithm Based Source Identification With Near-Field Scanning Method

Abstract-An innovative method for antenna arrays beam configuration is presented. In the proposed method, every element of the array is connected to its feed through a switch, so that it can be active or passive, depending on the switch position. Pattern reconfigurability is achieved by appropriately switching on or off the array elements. The optimal configuration of the switches for each of the radiated patterns, as well the common voltages of the active elements, is calculated by using a genetic algorithm. For each configuration, the currents in the driven and parasitic elements are determined, via their self and mutual impedances, by inversion of the impedance matrix. In the presented examples, the method has been applied to both linear and planar arrays of parallel dipoles that switch the power pattern from a pencil to a flat-topped beam (linear array) or to a footprint pattern (planar array).

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“…These optimization algorithms have been recently applied to many electromagnetic problems [15][16][17][18][19].…”

Section: Optimization By Using a Genetic Algorithmmentioning

confidence: 99%

A Simple Alternative for Beam Reconfiguration of Array Antennas

Ares‐Pena¹,

Franceschetti²,

Rodríguez³

2008

PIER

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“…The main performance of GA is based on composition, mutation, crossover and selection procedures [19][20][21][22][23][38][39][40][41][42][43]. The use of this search algorithm was applied to optimizing torque ripple, which has led to finding the interval of airgap in faulty and healthy conditions.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Method of Obtaining Degrees of Freedom for Radial Airgap Length in SRM Under Normal and Faulty Conditions Based on Magnetiostatic Model

Torkaman¹,

Afjei²

2010

PIER

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Abstract-In this paper, a new hybrid method of obtaining the degrees of freedom for redial airgap length in Switched Reluctance Motor operation under normal and faulty conditions based on magnetostatic analysis is presented. At the beginning, this method goes through the magnetic design of the motor utilizing three dimensional (3-D) Finite Element Method (FEM) in order to consider the end effects as well as axial fringing field effects. The motor parameters, such as torque, flux linkage, flux density versus rotor position are precisely obtained. Then, a Multi Layered Perception Neural Network is designed by considering the nonlinear behavior of the motor parameters obtained under different modes of operation. Using this network and the obtained parameters from FEM, an Objective Function (OF) for torque ripple with the aim of having a minimum mean square error is estimated. In addition, an improved Genetic Algorithm (GA) for the minimization the OF is also presented to determine the motor's operational regions. Finally, the legal intervals for different modes of motor operation are addressed.

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“…In the past few years, application of GA for solving various electromagnetic problems that require optimization or curve-fitting has gained popularity [21][22][23][24][25][26][27][28], including extraction dielectric properties of materials [13,14,[29][30][31]. GA is the most reasonable way of curve-fitting when specifically using rational-fractional functions, such as Debye terms, since it is easily formulated and programmable, robust, efficiently converging to a global minimum.…”

Section: Introductionmentioning

confidence: 99%

Planar Transmission Line Method for Characterization of Printed Circuit Board Dielectrics

Zhang¹,

Koledintseva²,

Antonini³

et al. 2010

PIER

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Abstract-An effective approach to characterize frequency-dispersive sheet materials over a wide RF and microwave frequency range based on planar transmission line geometries and a genetic algorithm is proposed. S-parameters of a planar transmission line structure with a sheet material under test as a substrate of this line are measured using a vector network analyzer (VNA). The measured S-parameters are then converted to ABCD matrix parameters. With the assumption of TEM/quasi-TEM wave propagation on the measured line, as well as reciprocity and symmetry of the network, the complex propagation constant can be found, and the corresponding phase constant and attenuation constant can be retrieved. Attenuation constant includes both dielectric loss and conductor loss terms. At the same time, phase term, dielectric loss and conductor loss can be calculated for a known transmission line geometry using corresponding closedform analytical or empirical formulas. These formulas are used to construct the objective functions for approximating phase constants, conductor loss and dielectric loss in an optimization procedure based on a genetic algorithm (GA). The frequency-dependent dielectric properties of the substrate material under test are represented as one or a few terms following the Debye dispersion law.The parameters of the Debye dispersion law are extracted using the GA by minimizing the discrepancies between the measured and the corresponding approximated loss and phase terms. The extracted data is verified by substituting these data in full-wave numerical modeling of structures containing these materials and comparing the simulated results with experimental.

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Adaptive Genetic Algorithm Based Source Identification With Near-Field Scanning Method

Cited by 29 publications

References 21 publications

A Simple Alternative for Beam Reconfiguration of Array Antennas

A Simple Alternative for Beam Reconfiguration of Array Antennas

Hybrid Method of Obtaining Degrees of Freedom for Radial Airgap Length in SRM Under Normal and Faulty Conditions Based on Magnetiostatic Model

Planar Transmission Line Method for Characterization of Printed Circuit Board Dielectrics

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